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以下展示的是《中国舰船研究》2025年第3期英文翻译版文章,题为
Low-noise optimization design for underwater series-connected multi-sphere composite shell structure
LI Hefan, ZHANG Guanjun, KE Yuzhao
Citation:
LI H F, ZHANG G J, KE Y Z. Low-noise optimization design for underwater series-connected multi-sphere composite shell structure[J]. Chinese Journal of Ship Research, 2025, 20(3): 118–124.https://english.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03619
(in both Chinese and English).水下串联多球组合壳结构低噪声优化设计
李贺帆, 张冠军, 柯昱照

To maximizing the working performance of the series-connected multi-sphere composite shell structure, an optimization design study has been conducted to enhance its acoustic and vibration performance.
First, a finite element model was developed to calculate and analyze the underwater acoustic radiation characteristics. Then, using the model's mass as the constraint condition and the radiation noise under vertical excitation as the optimization objective, a uniform experimental design was developed using the geometric parameters of the connecting structure as the design variables. The radial basis function (RBF) neural network was used to establish a multidimensional mapping model between the design variables and the optimization objective. The particle swarm optimization (PSO) algorithm was employed to optimize the underwater radiation noise of the model, and the results were verified through the finite element method. Finally, underwater acoustic radiation experiments were carried out, and the experimental test values were compared with the simulation values to validate the accuracy of the simulation results.
The results show that after optimization, the total underwater radiation sound power level of the series-connected multi-sphere composite shell under vertical excitation was reduced by 2.92 dB, while the mass was decreased by 0.061 t.
The research provides new insights into the optimization of low-noise structural design methods.
Keywords: shell (structure) /spherical shell/composite shell/radial basis function (RBF) neural networks/particle swarm optimization (PSO) /noise/design optimization/noise optimization
为充分发挥串联多球组合壳结构的工作性能,针对其声振性能开展优化设计研究。
首先,建立有限元模型,计算分析水下串联多球组合壳的水下声辐射特性;然后,以模型质量为约束条件,以垂向激励作用下辐射噪声为优化目标,建立以连接结构几何参数为设计变量的均匀试验设计,随后采用径向基函数(RBF)神经网络建立设计变量与优化目标之间的多维映射模型,使用粒子群优化(PSO)算法对模型水下辐射噪声进行优化,并通过有限元方法进行验证;最后,开展水下声辐射实验,并将试验测试值与仿真值进行对比,以证明仿真结果的准确性。
结果显示,经优化,垂向激励下串联多球组合壳的水下辐射声功率总级降低了2.92 dB,质量降低了0.061 t。
所做研究可为结构低噪声优化设计方法提供新的思路。
关键词: 壳体/球形壳体/组合壳/径向基函数神经网络/粒子群优化/噪声/ 设计优化/噪声优化

图1 串联多球组合壳流体域有限元模型
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编辑部整理了2019年以来出版的英文双语论文,每篇均对应出版了中英文两种语言版本,并按专业进行了分类。请点击“2019年以来双语版论文汇总”查看。



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